Modelling Customer Trajectories with Reinforcement Learning for Practical Retail Insights

In three linesReinforcement learning framework for predicting customer trajectories in retail spaces. RL-based approach outperforms TSP/PNN heuristics (average 28% deviation from shortest paths) by modeling bounded rationality. Validated on real convenience store data: RL predictions better align with observed behavior, more accurate impulse purchase rates and shelf traffic estimates, enabling practical layout optimization.

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